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Shadow Removal
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Image Shadow Removal
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Retinex-guided Histogram Transformer for Mask-free Shadow Removal
FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors
Leveraging Contrast Information for Efficient Document Shadow Removal
Prompt-Aware Controllable Shadow Removal
Towards Hard and Soft Shadow Removal via Dual-Branch Separation Network and Vision Transformer
Detail-Preserving Latent Diffusion for Stable Shadow Removal
Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation
Regional Attention for Shadow Removal
WavShadow: Wavelet Based Shadow Segmentation and Removal
ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal
Generative Portrait Shadow Removal
OmniSR: Shadow Removal under Direct and Indirect Lighting
SoftShadow: Leveraging Penumbra-Aware Soft Masks for Shadow Removal
Shadow Removal Refinement via Material-Consistent Shadow Edges
Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal
Diff-Shadow: Global-guided Diffusion Model for Shadow Removal
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal
Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal
ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal
S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal
ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer
Accurate Tennis Court Line Detection on Amateur Recorded Matches
Towards Image Ambient Lighting Normalization
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks
FieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics
NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
0-1 laws for pattern occurrences in phylogenetic trees and networks
Recasting Regional Lighting for Shadow Removal
Neural Spline Fields for Burst Image Fusion and Layer Separation
Latent Feature-Guided Diffusion Models for Shadow Removal
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models
Progressive Recurrent Network for Shadow Removal
Controlling Vision-Language Models for Multi-Task Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.